We propose to create a massively scalable toolkit to enable large, multi-center Patient-centered Information Commons (PIC) at local, regional and, national scale, where the focus is the alignment of all available biomedical data per individual. Such a Commons is a prerequisite for conducting the large-N, Big Data, longitudinal studies essential for understanding causation in the Precision Medicine framework while simultaneously addressing key complexities of Patient Centric Outcome Research studies required under ACA (Affordable Care Act). This agenda entails the four following aims: Aim 1: Create an individual patient data identification and retrieval toolkit that is robust across distributed data of wide variety and geographically scattered. Robustness with regard to a variety of organizational structures and national scalability is emphasized. Aim 2: Generate a complete diagnostic and prognostic 'data' picture of a patient across multiple sources of data, some of which are noisy and sparse. Aim 3. Enable robust decentralized computation on large-scale data with the Patient-centered Information Commons Big Data Science Platform (PIC-DSP), particularly in configurations where data are generated in locations other than where computational resources are most available. Aim 4: Create three patient-centered information commons instances (PICIs) to test all aspects of the toolkit developed. We have selected neurodevelopmental disorders as our first PICI, as it fulfills several criteria (wide variety of data types and scales, collaborator engagement, multiple healthcare institutions, and opportunity to rigorously test and refine features of the tool).